使用自然语言处理去识别临床自由文本:对当前方法的系统审查
Aleksandar Kovačević1, Bojana Bašaragin2, Nikola Milošević3
1The University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21002 Novi Sad, Serbia.
Artificial intelligence in medicine
|March 31, 2024
概括
电子健康记录 (EHR) 的非识别已经发展,深度学习模型现在实现了高性能. 然而,需要进一步评估,以确保安全,现实世界的数据去识别用于研究.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 数据 隐私 数据 隐私 数据
背景情况:
- 电子健康记录 (EHR) 对医学研究至关重要,但包含受保护的健康信息 (PHI).
- 取消身份识别对于删除PHI和使EHR数据共享用于研究至关重要.
- 自然语言处理 (NLP) 在自动化EHR非识别方面表现有前途.
研究的目的:
- 系统地审查13年来英语临床免费文本去识别的演变.
- 报告当前最先进的非识别系统的性能和局限性.
- 确定在电子健康记录 (EHR) 消除身份方面的挑战和未来的研究机会.
主要方法:
- 在PubMed,Web of Science和DBLP (2010年1月至2023年2月) 进行了系统的文献搜索.
- 分析了69项相关研究,重点关注去识别方法,数据源和绩效指标.
- 将方法分类为机器学习,混合和基于规则的系统.
主要成果:
- 机器学习 (37项研究) 和混合 (26项研究) 方法占主导地位.
- 2014年i2b2/UTHealth数据库是最常用的数据集 (36项研究).
- 最近的深度学习模型,特别是基于注意力的神经网络,实现了高F1得分 (>98%).
结论:
- 不识别方法已经从基于规则的混合物发展到复杂的神经网络.
- 当前最先进的系统表现出高性能,但需要进一步验证现实世界的可靠性.
- 跨不同临床领域的概括性仍然是一个挑战,如果没有足够的标记训练数据.
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